Modern complex systems can place both high and low levels of cognitive demands on human operators. If operators do not maintain an optimal cognitive state, then overall system performance may suffer. For example, if an operator is in a relatively high cognitive state, the likelihood that the operator may make an error increases and system performance may degrade. As a result, when the suitability of new or modified systems is tested, accurate assessment of operator functional state is required. If the system places demands on the operator beyond his or her capabilities, then the system or operational procedures must be modified. On the other hand, high levels of automation can lead to complacency, which may result in the operator losing awareness of the system's true state. This situation can also lead to reduced performance and increased errors. In some circumstances it may be possible for the system to adapt the task to meet the momentary needs of the operator. For example, if the operator is becoming mentally overloaded, the system may be able to automate some aspects of the task to reduce the cognitive demands on the operator. The reduced task demands should lead to reduced operator mental workload and enhanced performance. Accurate estimations of operator functional state are thus desirable in these situations.
From the foregoing, it may be appreciated that operator state classification is becoming increasingly used across multiple domains. However, current operator state classification systems classify a general operator state, such as workload state, into one of two classification states—high workload state and low workload state. This is because known two-state classifiers generally provide relatively robust classification performance. While it is generally desired to classify greater than two states of a given dimension, various research has demonstrated a decrement when attempting to do. For example, one set of researchers documented that discriminating four levels of workload state yielded classification performance between 84-88%, whereas two levels, using the same data, yielded classification performance of about 98%.
One of the goals of next generation adaptive systems is the capability to implement more precise adaptations. It is believed that this goal may be realized if three, four, or even more levels of a user states can be distinguished. For example, as was noted above, current classification systems may classify operator workload state as either a low workload state or a high workload state. This is not only because these two states are distinct and have operational relevance, but because they are relatively easy to distinguish. However, there may be some instances where knowing that an operator is experiencing nominal workload, compared to low or high workload, could provide higher resolution state tracking and adaptation selection. However, to insure user acceptance and trust, classification performance above 90% will likely be needed.
Hence, there is a need for a system and method that provides increased state level discrimination, as compared to presently known multi-level classifiers. The present invention addresses at least this need.